A cargo risk assessment and early warning method, system, device and storage medium for logistics robots

Abstract

The embodiment of the present application provides a cargo risk assessment and early warning method, system, device and storage medium for a logistics robot, the method comprising the following steps: obtaining the first item information to be identified in real time; determining the risk category of the first item based on the obtained first item information; determining the comprehensive risk value of the first item based on the risk category of the first item; obtaining the logistics response plan of the first item based on the comprehensive risk value of the first item. The embodiment of the present application is more intelligent when loading cargo, and can analyze the detected cargo, so as to adopt different loading methods for different cargoes, which is more convenient to use.

Description

Technical Field

[0001] The embodiments of the present application belong to the technical field of logistics robots, and in particular, relate to a cargo risk assessment and early warning method, system, device and storage medium for logistics robots. Background Art

[0002] Logistics robots are used to realize the logistics delivery of parcels, letters, catering and other goods in closed office buildings, parks, communities, campuses, etc. that are inconvenient for deliverymen and couriers to enter.

[0003] The logistics robot itself is an intelligent device with the necessary hardware and software configurations. It can realize the autonomous execution of all operating links in the logistics distribution process without the need for remote control by the operation center. The autonomously executed operating links include but are not limited to: task order acceptance, cargo inspection, path planning, laser radar surrounding scene perception, navigation and obstacle avoidance, etc.

[0004] During the operation of some existing logistics robots, risk assessment of the goods they carry is an important step in the item inspection process. However, when conducting risk assessment on the goods they carry, the existing logistics robots have a relatively simple assessment method. On the other hand, when the detected goods do not meet the transportation requirements, loading is generally prohibited. Therefore, the functions of logistics robots are relatively simple and the transportation of goods is relatively limited, so they are in urgent need of improvement. Summary of the invention

[0005] The purpose of the embodiments of the present application is to provide a cargo risk assessment and early warning method, system, device and storage medium for a logistics robot, which obtains the information of the first item to be identified in real time, determines the risk category of the first item, and then determines the comprehensive risk value of the first item, thereby obtaining a logistics response plan for the first item. When loading the goods, it is more intelligent and can analyze the detected goods, so as to adopt different loading methods for different goods, which is more convenient to use, thereby solving the problems in the background technology.

[0006] In order to solve the above technical problems, the technical solutions of the cargo risk assessment and early warning method, system, device and storage medium for logistics robots provided in the embodiments of the present application are as follows:

[0007] In a first aspect, an embodiment of the present application discloses a cargo risk assessment and early warning method for a logistics robot, the method comprising the following steps:

[0008] Acquire the first item information to be identified in real time;

[0009] Determining a risk category of the first item based on the acquired first item information;

[0010] Determining a comprehensive risk value of the first item according to the risk category of the first item;

[0011] A logistics response plan for the first item is obtained according to the comprehensive risk value of the first item.

[0012] In a preferred embodiment of any of the above solutions, the real-time acquisition of the first object information to be identified specifically includes the following steps:

[0013] Sending a command to the user to adjust the angle of the first object;

[0014] According to different angles of the first object, image information of at least one angle of the first object is acquired.

[0015] In a preferred embodiment of any of the above schemes, the instruction is at least one of voice information, text information, video information or action information.

[0016] In a preferred embodiment of any of the above solutions, determining the risk category of the first item specifically includes the following steps:

[0017] Inputting image information of at least one angle of the first object into an object classifier to obtain a specific category of the first object;

[0018] The specific category of the first article is compared with at least one preset cargo risk category to determine the risk category of the first article.

[0019] In a preferred embodiment of any of the above solutions, the cargo risk category is preset in the cargo risk knowledge graph;

[0020] Determining the comprehensive risk value of the first item specifically includes the following steps:

[0021] According to the cargo risk category and cargo risk knowledge graph, the comprehensive risk value assessment standard is called, and then according to the specific category of the goods, the specific comprehensive risk value is obtained by comparing the assessment standard.

[0022] In a preferred embodiment of any of the above schemes, the cargo risk category is:

[0023] It is prohibited to transport items, transport risk items, sensitive risk items, high-value or important items, items that may damage and contaminate the robot.

[0024] In a preferred embodiment of any of the above schemes, the logistics response scheme for the first article specifically includes the following steps:

[0025] determining a risk category of the first item;

[0026] comparing the risk category of the first object with a corresponding assessment criterion;

[0027] Obtaining a comprehensive risk value of the first item by comparing the risk category with the corresponding assessment standard;

[0028] If the comprehensive risk value of the first item is 1-10, it is a level one risk; if the comprehensive risk value of the first item is 11-20, it is a level two risk; if the comprehensive risk value of the first item is 21-30, it is a level three risk; if the comprehensive risk value of the first item is 31-40, it is a level four risk; if the comprehensive risk value of the first item is 50, it is a level five risk.

[0029] Compared with the prior art, the cargo risk assessment and early warning method for a logistics robot in the embodiment of the present application obtains the information of the first item to be identified in real time, determines the risk category of the first item, and then determines the comprehensive risk value of the first item, thereby obtaining a logistics response plan for the first item. When loading goods, it is more intelligent and can analyze the detected goods, thereby adopting different loading methods for different goods, which is more convenient to use.

[0030] In the second aspect, a cargo risk assessment and early warning system for a logistics robot includes:

[0031] The acquisition module is used to acquire the information of the first object to be identified in real time, wherein the real-time acquisition of the information of the first object to be identified specifically includes the following steps: sending an instruction to the user to adjust the angle of the first object; acquiring image information of at least one angle of the first object according to different angles of the first object, wherein the instruction is at least one of voice information, text information, video information or action information;

[0032] A first determination module is used to determine the risk category of the first item according to the acquired first item information, wherein the determination of the risk category of the first item specifically comprises the following steps: inputting image information of at least one angle of the first item into an item classifier to obtain a specific category of the first item; comparing the specific category of the first item with at least one preset cargo risk category to determine the risk category of the first item, wherein the cargo risk category is preset in a cargo risk knowledge graph;

[0033] The second determination module determines the comprehensive risk value of the first item according to the risk category of the first item, wherein the determination of the comprehensive risk value of the first item specifically comprises the following steps: calling a comprehensive risk value assessment standard according to the cargo risk category and the cargo risk knowledge graph, and then obtaining a specific comprehensive risk value according to the specific category of the item and comparing the assessment standard;

[0034] The response module is used to obtain a logistics response plan for the first item according to the comprehensive risk value of the first item, and the logistics response plan for the first item specifically includes the following steps: determining the risk category of the first item; comparing the risk category of the first item with the corresponding evaluation standard; obtaining a comprehensive risk value of the comparison between the risk category of the first item and the corresponding evaluation standard; if the comprehensive risk value of the first item is 1-10, it is a first-level risk; if the comprehensive risk value of the first item is 11-20, it is a second-level risk; if the comprehensive risk value of the first item is 21-30, it is a third-level risk; if the comprehensive risk value of the first item is 31-40, it is a fourth-level risk; if the comprehensive risk value of the first item is 50, it is a fifth-level risk.

[0035] The beneficial effects of the second aspect are consistent with those of the first aspect, so they will not be described in detail here.

[0036] In a third aspect, a cargo risk assessment and early warning device for a logistics robot includes:

[0037] Memory for storing computer programs;

[0038] A processor is used to implement the steps of the cargo risk assessment and early warning method for a logistics robot when executing the computer program.

[0039] The beneficial effects of the third aspect are consistent with those of the first aspect, so they will not be described in detail here.

[0040] In a fourth aspect, a storage medium stores a computer program, which, when executed by a processor, implements the cargo risk assessment and early warning method for a logistics robot.

[0041] The fourth aspect has the same beneficial effects as the first aspect, so it will not be described in detail here. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] The drawings described herein are used to provide a further understanding of the present application and constitute a component of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. Some specific embodiments of the present application will be described in detail in an illustrative and non-restrictive manner with reference to the drawings. The same reference numerals in the drawings indicate the same or similar components or components. It should be understood by those skilled in the art that these drawings are not necessarily drawn to scale. In the drawings:

[0043] Figure 1 This is a flow chart of a cargo risk assessment and early warning method for a logistics robot according to an embodiment of the present application.

[0044] Figure 2This is a schematic diagram of the process of obtaining the first item information to be identified in real time in the cargo risk assessment and early warning method for a logistics robot in an embodiment of the present application.

[0045] Figure 3 This is a schematic diagram of the process of determining the risk category of a first item in the cargo risk assessment and early warning method for a logistics robot in an embodiment of the present application.

[0046] Figure 4 This is a schematic diagram of the process of determining the comprehensive risk value of a first item in the cargo risk assessment and early warning method for a logistics robot according to an embodiment of the present application.

[0047] Figure 5 This is a flow chart of the logistics response plan for the first item in the cargo risk assessment and early warning method for a logistics robot in an embodiment of the present application.

[0048] Figure 6 Schematic diagram of a cargo risk assessment and early warning system for a logistics robot according to an embodiment of the present application.

[0049] Figure 7 Schematic diagram of cargo risk assessment and early warning equipment used for a logistics robot according to an embodiment of the present application. DETAILED DESCRIPTION

[0050] In order to enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only embodiments of a component of the present application, rather than embodiments of the entire component. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work should fall within the scope of protection of the present application.

[0051] Example

[0052] like Figure 1 As shown, an embodiment of the present application provides a cargo risk assessment and early warning method for a logistics robot, the method comprising the following steps:

[0053] Step 1: Acquire the first object information to be identified in real time;

[0054] Step 2: Determine the risk category of the first item based on the acquired first item information;

[0055] Step 3: Determine the comprehensive risk value of the first item according to the risk category of the first item;

[0056] Step 4: Obtain a logistics response plan for the first item according to the comprehensive risk value of the first item.

[0057] In the cargo risk assessment and early warning method for a logistics robot described in an embodiment of the present invention, the information of the first item to be identified is obtained in real time, and the risk category of the first item is determined, and then the comprehensive risk value of the first item is determined, so as to obtain a logistics response plan for the first item. When loading the goods, it is more intelligent and can analyze the detected goods, so as to adopt different loading methods for different goods, which is more convenient to use.

[0058] like Figure 2 As shown, the real-time acquisition of the first object information to be identified specifically includes the following steps:

[0059] Step 11: Sending an instruction to the user to adjust the angle of the first object;

[0060] Step 12: Acquire image information of at least one angle of the first object according to different angles of the first object.

[0061] In the cargo risk assessment and early warning method for a logistics robot described in an embodiment of the present invention, when in use, in order to achieve more accurate classification of items, when collecting information about items, it is necessary to identify images of items at multiple angles, wherein, in order to collect information at multiple angles of the image, an instruction can be sent to the user to adjust the angle of the item, wherein one of voice information, text information, video information or action information can be sent to the user, so that the user can adjust the item, for example, a voice instruction can be sent, "Please turn the item to the left more than 30 degrees", so as to collect images of the item, when the voice prompt effect is not good, the voice prompt can be combined with the video information, for example, an operation example is displayed on the display, so that the user follows the example on the display to operate, so as to adjust the angle of the item, and then collect images of the item, for example, the image can also be combined with text information, so that the operation video is displayed on the display, and then the text information is referred to, so that the angle of the item is adjusted, thereby realizing multi-angle collection of image information.

[0062] like Figure 3 As shown, determining the risk category of the first item specifically includes the following steps:

[0063] Step 21: inputting the image information of at least one angle of the first object into an object classifier to obtain a specific category of the first object;

[0064] Step 22: Compare the specific category of the first item with at least one preset cargo risk category to determine the risk category of the first item, wherein the cargo risk category is preset in the cargo risk knowledge graph, and the cargo risk categories are: prohibited items for transportation, transportation risk items, sensitive risk items, high-value or important items, and items that may damage and contaminate the robot, wherein the knowledge graph is a triple data structure with "entity-relationship-entity" as the basic structure, and different "entities" can be associated through "relationship" as a link to achieve efficient and comprehensive information query; in the cargo risk, "prohibited items for transportation, transportation risk items, sensitive risk items, high-value or important items, and items that may damage and contaminate the robot" and other "cargo risk categories" belong to "entities", and the specific category of the item also belongs to "entities", and the two form a knowledge graph through the "relationship" of the specific category to the cargo risk category, so that according to the label of the specific category of the first item output by the item classifier, its "cargo risk category" can be obtained through the cargo risk knowledge graph.

[0065] In the cargo risk assessment and early warning method for a logistics robot described in an embodiment of the present invention, the item classifier can be implemented by using a classifier with a neural network as the core, such as ResNET, VGG, Alexnet, etc. Of course, the item classifier is specially trained using an item image training set collected for various items in the logistics field, and can output the classification label of the specific category to which the item belongs based on the image information of the first item; of course, due to the large number of items and diverse situations encountered in the logistics field, multiple item classifiers can be connected in parallel, and the classification labels of multiple item classifiers can be combined together. For example, crystal jewelry is both a high-value item and a transportation risk item. By comparing it with at least one cargo risk category, a more accurate classification of items can be achieved. For example, the classification of items can be firecrackers, detonators, eggs, glass bottles, ice cream, watches, mobile phones, identity cards, real estate certificates, magnetic core coils, etc. Then, according to the preset cargo risk knowledge graph, the specific category of the item is matched to the "prohibited The prohibited items are: items that are highly dangerous in terms of safety and are prohibited from being transported by logistics robots, such as flammable and explosive items; the transport risk items are: items that are easily damaged during the logistics process and have transportation risks, such as glass products or fragile items packaged in glass, eggs, items containing liquids that are easy to leak or spill, etc.; the sensitive risk items are: items that are time-sensitive risks, such as ice products, cold chain items or medicines; the high-value or important items are: items that are high-value or important, such as certificates, high-end electronic products such as laptops, famous bags, watches; the items that can damage and contaminate robots are: items that may cause damage and contamination to the robot itself, such as items that may have strong electromagnetic interference, items that may contaminate the robot's cargo warehouse, corrupt or other items that may have odors, etc.

[0066] like Figure 4 As shown, the determination of the comprehensive risk value of the first item specifically includes the following steps:

[0067] Step 31: According to the cargo risk category and cargo risk knowledge graph, call the comprehensive risk value assessment standard;

[0068] Step 32: According to the specific category of the item, compare with the assessment standard to obtain a specific comprehensive risk value.

[0069] In the cargo risk assessment and early warning method for a logistics robot described in an embodiment of the present invention, the comprehensive risk value under the cargo risk category is determined according to the cargo risk category and the specific category of the article (according to the cargo risk category, according to the cargo risk knowledge graph, the comprehensive risk value assessment standard is called; then according to the specific category of the article, the assessment standard is compared to obtain a specific comprehensive risk value). The classification and identification of the articles can adopt a pre-trained classification neural network, generally having a multi-layer convolutional neural network and a classifier with an output category probability distribution. Currently, the more common ones are ResNet, Alexnet, etc.

[0070] The "comprehensive risk value assessment standard" also adopts the data structure of the knowledge graph, in which each "cargo risk category" corresponds to its own specific "comprehensive risk value assessment standard". For example, the "comprehensive risk value assessment standard" corresponding to "transport risk goods" includes assessment dimension entities such as "goods damage rate" and "goods damage discount rate". The specific category of the goods is also taken as an "entity" to establish an association relationship with the above-mentioned assessment dimension entities such as "goods damage rate" and "goods damage discount rate". For another example, the "comprehensive risk value assessment standard" corresponding to "time-sensitive risk goods" includes assessment dimension entities such as "goods delay sensitivity", "goods delay damage rate" and "goods delay damage discount rate". The specific category of the goods is also taken as an "entity" to establish an association relationship with the above-mentioned assessment dimension entities; and, according to the specific category of the goods, a comprehensive risk value is assigned to each assessment dimension entity. For example, if the specific category of the goods is glass products, a comprehensive risk value is assigned to glass products in the dimensions of "goods damage rate" and "goods damage discount rate", and then the comprehensive risk values ​​are accumulated as the comprehensive risk value of the specific category of the goods. Among them, according to the level of comprehensive risk value, it can be divided into different levels, such as level four to level one.

[0071] like Figure 5 As shown, the logistics response plan for the first item specifically includes the following steps:

[0072] Step 41: Determine the risk category of the first item;

[0073] Step 42: Compare the risk category of the first article with the corresponding assessment criteria;

[0074] Step 43: Obtaining a comprehensive risk value of the risk category of the first item compared with the corresponding assessment standard;

[0075] Step 44: If the comprehensive risk value of the first item is 1-10, it is a level one risk; if the comprehensive risk value of the first item is 11-20, it is a level two risk; if the comprehensive risk value of the first item is 21-30, it is a level three risk; if the comprehensive risk value of the first item is 31-40, it is a level four risk; if the comprehensive risk value of the first item is 50, it is a level five risk.

[0076] In the cargo risk assessment and early warning method for a logistics robot described in an embodiment of the present invention, the logistics robot determines the comprehensive risk value of the cargo according to the cargo risk category, and obtains the logistics response plan of the cargo by comparing it with the cargo risk knowledge graph, wherein the higher the level, the greater the transportation risk, wherein the response plan includes the following response measures and their combinations:

[0077] For example, if the goods to be transported are flammable or explosive, the comprehensive risk value of the goods is 50, and they are divided into five levels of risk. The logistics response plan corresponding to the fifth level of risk is prohibiting loading.

[0078] For example, if the goods are transport risk items, that is, items that are easily damaged during the logistics process and have transportation risks, the items that are easily damaged during the logistics process and have transportation risks may be glass products or fragile items packaged in glass, eggs, and items that contain liquids that are easy to leak or spill. Then, glass products, fragile items packaged in glass, eggs, and items that contain liquids that are easy to leak or spill can be divided into different risk levels according to their respective comprehensive risk values, and then the corresponding logistics response plan for the goods is found according to their respective comprehensive risk values: for example, the comprehensive risk value of glass products is 8, so the glass products are classified as level one risk. Before loading the glass products, it is necessary to verify whether the glass products have adopted additional protection to prevent damage or maintain the direction of the glass products. If a protective structure is added, the logistics robot will transport them. For another example, the comprehensive risk value of items that contain liquids that are easy to leak is 11, so the items that contain liquids that are easy to leak are classified as level two risk, and then it is necessary to verify whether the items that contain liquids that are easy to leak have adopted additional protection to prevent damage. If a protective structure is added, the logistics robot will transport them. The comprehensive risk value is determined according to the risk level of the goods themselves.

[0079] For example, if the goods are sensitive risk items, that is, items with time-sensitive risks, such as ice products, cold chain items or medicines, then first determine the comprehensive risk value of the items according to their own conditions. If they are some items with strict time requirements, then the comprehensive risk value of the items is relatively high. For example, the goods to be transported are some test samples or medicines, which have strict time requirements and cannot be delayed. Then the comprehensive risk value is 40, and the goods are divided into four levels of risk. When transporting, set the delivery time limit, start the countdown, and when the countdown approaches the delivery time limit, if the delivery is still not completed, For example, if the goods to be transported are some ice products, which have strict time requirements, but can still be used after a certain delay, the comprehensive risk value is 18, and the goods are classified as secondary risk. Then, when transporting, set a delivery time limit, start a countdown, and when the countdown approaches the delivery time limit, if the delivery is still not completed, set a delay time interval again. If the delivery is completed within the delay time interval, it is fine. If the delivery is not completed within the delay time interval, an alarm is sent to the management background of the logistics robot to request human intervention.

[0080] For example, if the goods are high-value or important items, such as documents, high-end electronic products such as laptops, famous bags, watches, etc., the comprehensive risk value is first determined according to the value of the items. If the value is relatively high, for example, high-end electronic products, then the comprehensive risk value is 28, and the goods are divided into three levels of risk. Then, when transporting, the user is prompted to apply for loss insurance or confirm the loss exemption clause through the pop-up menu on the logistics robot display. If not, loading is prohibited; and high-security logistics routes or logistics measures are adopted, such as logistics routes covered by video surveillance without blind spots, and video recording of the entire logistics distribution process. During the transportation process, the management background is prompted to conduct remote tracking and monitoring of the entire logistics process.

[0081] For example, if the goods are items that may damage and contaminate the robot, such as: items that may have strong electromagnetic interference, items that may contaminate the robot's cargo hold, corrupt or other items that may have odors, etc., first determine the comprehensive risk value based on the degree of risk the items pose to the robot. If the risk is relatively high, for example, items that may have strong electromagnetic interference, then the comprehensive risk value is 32, and the goods are classified into four levels of risk. Then, when transporting, it is necessary to add an additional base or sheath to the goods to wrap the goods and shield the strong electromagnetic interference. If protection has been taken and no strong electromagnetic interference is generated, loading is allowed. If no protection is taken, loading is prohibited.

[0082] like Figure 6As shown, in the second aspect, a cargo risk assessment and early warning system for a logistics robot comprises:

[0083] A construction module is used to obtain the robot's running process data in real time, and to construct abnormal risk parameters based on the running process data, to obtain the data accumulated by the robot during operation, so as to obtain the running process data of the robot in abnormal conditions in any aspect of operation execution, spatial position, travel, surrounding environment, robot action, and equipment component status, and to use the running process data as a sample for constructing abnormal risk parameters;

[0084] A processing module is used to collect and decompose the running process data to obtain real-time data features and real-time scene parameters of the real-time running process data; divide the real-time data features and real-time scene parameters into real-time data feature fields and sub-field real-time scene parameters corresponding to each real-time data feature field;

[0085] A comparison module is used to compare the decomposed running process data with the constructed abnormal risk parameters, and to compare the real-time data features of the robot when it is running with the abnormal data feature fields of the same field type according to each real-time data feature field to obtain real-time data feature fields with high similarity; extract the sub-field real-time scene parameters corresponding to the real-time data feature fields with high similarity; and divide the risk level according to the coverage ratio of the sub-field real-time scene parameters and the minimum set of the sub-field scene parameter set;

[0086] The switching module is used to respond to the abnormal risk level according to the comparison result and switch the operation authority mode. In response to the abnormal risk comparison judgment result, when the abnormal risk is at a high level, it is switched to the operation center to take over the operation; if the abnormal risk is at a medium level, based on the medium level, it is decided that part of the robot's operation will be taken over by the operation center and part will remain autonomous; if the abnormal risk is at a low level, the robot's autonomous mode will be maintained.

[0087] Thirdly, Figure 7 As shown, a cargo risk assessment and early warning device for a logistics robot includes: a memory for storing a computer program; a processor for implementing the steps of the cargo risk assessment and early warning method for a logistics robot when executing the computer program.

[0088] Wherein, the processor is used to control the overall operation of the measuring device to complete all or part of the steps in the above-mentioned cargo risk assessment and early warning method for logistics robots. The memory is used to store various types of data to support the operation of the measuring device, and these data may include, for example, instructions for any application or method used to operate on the measuring device, and application-related data, such as contact data, messages sent and received, pictures, audio, video, etc. The memory can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, referred to as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), erasable programmable read-only memory (Erasable Programmable Read-Only Memory, referred to as EPROM), programmable read-only memory (Programmable Read-Only Memory, referred to as PROM), read-only memory (Read-Only Memory, referred to as ROM), magnetic memory, flash memory, disk or optical disk. The multimedia component may include a screen and an audio component. Wherein the screen can be, for example, a touch screen, and the audio component is used to output and / or input audio signals. For example, the audio component can include a microphone, and the microphone is used to receive external audio signals. The received audio signal can be further stored in a memory or sent through a communication component. The audio component also includes at least one speaker for outputting audio signals. The I / O interface provides an interface between the processor and other interface modules, and the above-mentioned other interface modules can be keyboards, mice, buttons, etc. These buttons can be virtual buttons or physical buttons. The communication component is used for wired or wireless communication between the measuring device and other devices. Wireless communication, such as Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G, 4G or 5G, or a combination of one or more of them, so the corresponding communication component can include: Wi-Fi module, Bluetooth module, NFC module.

[0089] In an exemplary embodiment, the measuring device can be implemented by one or more application specific integrated circuits (Application Specific Integrated Circuit, referred to as ASIC), digital signal processors (Digital Signal Processor, referred to as DSP), digital signal processing devices (Digital Signal Processing Device, referred to as DSPD), programmable logic devices (Programmable Logic Device, referred to as PLD), field programmable gate arrays (Field Programmable Gate Array, referred to as FPGA), controllers, microcontrollers, microprocessors or other electronic components to execute the above-mentioned cargo risk assessment and early warning method for logistics robots.

[0090] In another exemplary embodiment, a computer-readable storage medium including program instructions is also provided, and when the program instructions are executed by a processor, the steps of the above-mentioned cargo risk assessment and early warning method for a logistics robot are implemented. For example, the computer-readable storage medium can be the above-mentioned memory including program instructions, and the above-mentioned program instructions can be executed by a processor of a measuring device to complete the above-mentioned cargo risk assessment and early warning method for a logistics robot.

[0091] Corresponding to the above method embodiment, the embodiment of the present disclosure also provides a readable storage medium. The readable storage medium described below and the cargo risk assessment and early warning method for a logistics robot described above can be referenced to each other.

[0092] In a fourth aspect, a readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the cargo risk assessment and early warning method for a logistics robot of the above-mentioned method embodiment.

[0093] The readable storage medium may specifically be a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, or other readable storage medium that can store program codes.

[0094] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for the technical features of the components or all of the components therein. These modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A cargo risk assessment and early warning method for a logistics robot, characterized in that: The method comprises the following steps: Acquire the first item information to be identified in real time; Determining a risk category of the first item based on the acquired first item information; Determining a comprehensive risk value of the first item according to the risk category of the first item; A logistics response plan for the first item is obtained according to the comprehensive risk value of the first item.

2. The cargo risk assessment and early warning method for a logistics robot according to claim 1 is characterized in that: The real-time acquisition of the first object information to be identified specifically includes the following steps: Sending a command to the user to adjust the angle of the first object; According to different angles of the first object, image information of at least one angle of the first object is acquired.

3. The cargo risk assessment and early warning method for a logistics robot according to claim 2 is characterized in that: The instruction is at least one of voice information, text information, video information or action information.

4. The cargo risk assessment and early warning method for a logistics robot according to claim 3 is characterized in that: Determining the risk category of the first item specifically includes the following steps: Inputting image information of at least one angle of the first object into an object classifier to obtain a specific category of the first object; The specific category of the first article is compared with at least one preset cargo risk category to determine the risk category of the first article.

5. The cargo risk assessment and early warning method for a logistics robot according to claim 4 is characterized in that: The cargo risk category is preset in the cargo risk knowledge graph; Determining the comprehensive risk value of the first item specifically includes the following steps: According to the cargo risk category and cargo risk knowledge graph, the comprehensive risk value assessment standard is called, and then according to the specific category of the goods, the corresponding item category assessment standard is compared to obtain a specific comprehensive risk value.

6. The cargo risk assessment and early warning method for a logistics robot according to claim 5, It is characterized in that The cargo risk categories are: It is prohibited to transport items, transport risk items, sensitive risk items, high-value or important items, items that may damage and contaminate the robot.

7. The cargo risk assessment and early warning method for a logistics robot according to claim 6 is characterized in that: The logistics response plan for the first item specifically includes the following steps: determining a risk category of the first item; comparing the risk category of the first object with a corresponding assessment criterion; Obtaining a comprehensive risk value of the risk category of the first item compared with a corresponding assessment standard; If the comprehensive risk value of the first item is 1-10, it is a level one risk; if the comprehensive risk value of the first item is 11-20, it is a level two risk; if the comprehensive risk value of the first item is 21-30, it is a level three risk; if the comprehensive risk value of the first item is 31-40, it is a level four risk; if the comprehensive risk value of the first item is 50, it is a level five risk.

8. A cargo risk assessment and early warning system for a logistics robot, characterized in that: include: The acquisition module is used to acquire the information of the first object to be identified in real time, wherein the real-time acquisition of the information of the first object to be identified specifically includes the following steps: sending an instruction to the user to adjust the angle of the first object; acquiring image information of at least one angle of the first object according to different angles of the first object, wherein the instruction is at least one of voice information, text information, video information or action information; A first determination module is used to determine the risk category of the first item according to the acquired first item information, wherein the determination of the risk category of the first item specifically comprises the following steps: inputting image information of at least one angle of the first item into an item classifier to obtain a specific category of the first item; comparing the specific category of the first item with at least one preset cargo risk category to determine the risk category of the first item, wherein the cargo risk category is preset in a cargo risk knowledge graph; The second determination module determines the comprehensive risk value of the first item according to the risk category of the first item, wherein the determination of the comprehensive risk value of the first item specifically comprises the following steps: calling a comprehensive risk value assessment standard according to the cargo risk category and the cargo risk knowledge graph, and then obtaining a specific comprehensive risk value according to the specific category of the item and comparing the assessment standard; The response module is used to obtain a logistics response plan for the first item according to the comprehensive risk value of the first item, and the logistics response plan for the first item specifically includes the following steps: determining the risk category of the first item; comparing the risk category of the first item with the corresponding evaluation standard; obtaining a comprehensive risk value of the comparison between the risk category of the first item and the corresponding evaluation standard; if the comprehensive risk value of the first item is 1-10, it is a first-level risk; if the comprehensive risk value of the first item is 11-20, it is a second-level risk; if the comprehensive risk value of the first item is 21-30, it is a third-level risk; if the comprehensive risk value of the first item is 31-40, it is a fourth-level risk; if the comprehensive risk value of the first item is 50, it is a fifth-level risk.

9. A cargo risk assessment and early warning device for a logistics robot, characterized in that: include: Memory for storing computer programs; A processor, used to implement the steps of the cargo risk assessment and early warning method for a logistics robot as described in any one of claims 1 to 7 when executing the computer program.

10. A storage medium, characterized in that: A computer program is stored thereon, characterized in that when the program is executed by a processor, the cargo risk assessment and early warning method for a logistics robot as described in any one of claims 1 to 7 is implemented.

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